A Novel Hybrid GA-PSO Algorithm-Based Optimization of Transmission and Expansion Planning

Abstract

In the power system environment, transmission and expansion planning (TNEP) is an essential and computationally very challenging problem in power systems. Competent and robust optimization techniques are required to get the optimal solution technically and economically. This paper aims to resolve the transmission and expansion planning problem in less computational time and investment costs using H1GAPSO and H2GAPSO algorithms. Two hybrid progressive algorithms based on the combination of PSO and GA methods are proposed and crossing over the PSO and GA have been implemented in this paper. The focus behind the proposed methods is to merge PSO and GA methods in a combination of parallel and series form, respectively. To validate the proposed hybrid algorithm and to test efficacy in comparison with other methods reported in the literature, it is tested on Garver’s-6 bus, IEEE-14 bus, and IEEE-24 bus test systems using MATLAB. For IEEE-14 and IEEE-24 bus systems, by applying the hybridization, the optimal investment costs are reduced to 520 US$ and 630 US$, respectively and the corresponding computational time in seconds are reduced to 4.3637 s and 4.3788 s. For Garver’s 6 bus system, the computational time are 1.4936 s and 1.1847 s for both hybridization. The results are compared with conventional GA and PSO methods. The simulation and observations of the outcome demonstrate the effectiveness of the proposed hybrid algorithms' time and have the better ability to find the global optimum solution. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.